Evidence map›Paper›PMID 42086725›Full record

ArticleCommunications medicine2026

Comprehensive evaluation of cross cancer generalization in histopathology segmentation models across 21 tumor types.

Tillmann Bedau, Christian Harder, Abdulkader Al-Shughri, Yuan Wang, Alexey Pryalukhin, Marie-Lisa Eich, Su Ir Lyu, Reinhard Büttner, Alexander Quaas, Yuri Tolkach

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Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Tillmann BedauInstitute of Pathology, University Hospital Cologne, Cologne, Germany; Medical Faculty, University of Cologne, Cologne, Germany.ORCID http://orcid.org/0000-0001-7510-2261
Christian HarderInstitute of Pathology, University Hospital Cologne, Cologne, Germany; Medical Faculty, University of Cologne, Cologne, Germany.
Abdulkader Al-ShughriInstitute of Pathology, University Hospital Cologne, Cologne, Germany; Medical Faculty, University of Cologne, Cologne, Germany.
Yuan WangInstitute of Pathology, University Hospital Cologne, Cologne, Germany; Medical Faculty, University of Cologne, Cologne, Germany.
Alexey PryalukhinInstitute of Pathology, University Hospital Wiener Neustadt, Danube Private University, Wiener Neustadt, Austria.
Marie-Lisa EichInstitute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany.
Su Ir LyuInstitute of Pathology, University Hospital Cologne, Cologne, Germany; Medical Faculty, University of Cologne, Cologne, Germany.
Reinhard BüttnerInstitute of Pathology, University Hospital Cologne, Cologne, Germany; Medical Faculty, University of Cologne, Cologne, Germany.ORCID http://orcid.org/0000-0001-8806-4786
Alexander QuaasInstitute of Pathology, University Hospital Cologne, Cologne, Germany; Medical Faculty, University of Cologne, Cologne, Germany.
Yuri TolkachInstitute of Pathology, University Hospital Cologne, Cologne, Germany; Medical Faculty, University of Cologne, Cologne, Germany. yuri.tolkach@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence has significantly advanced computational pathology by enabling high-resolution, clinical-grade tumor segmentation models with state-of-the-art diagnostic accuracy. Creating such models is resource-intensive, requiring substantial time and domain expertise. Additionally, deep learning models are typically restricted to single tumor types, making it challenging to develop separate models for each tumor type. Cross-cancer generalization of segmentation models could address this bottleneck and pave the way for pan-cancer segmentation models.

methodsWe evaluated the cross-tumor generalization capability of five tissue segmentation models (breast, colon, lung, kidney, prostate) using 21 cancer types from The Cancer Genome Atlas, totaling over 7,700 whole slide images. Representative large tumor and benign regions were manually selected, and segmentation accuracy was evaluated using a semiquantitative scale (0-10).

resultsHere we show that the lung model demonstrates excellent cross-cancer performance (overall mean score 7.9 ± 2.1), effectively segmenting tumor regions in many non-lung cancers with segmentation accuracy similar to its native domain in 11 of 19 other epithelial tumors and melanoma, achieving particularly strong results in ovarian cancer (9.2 ± 0.9). The breast and colon models also show strong cross-domain performance, while the kidney and prostate models exhibit more limited generalization. Overall, high-precision segmentation is achievable in most cancer types using existing models.

conclusionsExisting segmentation models generalize across multiple cancer types, reducing the need to develop new, entity-specific models from scratch. This cross-domain generalization enables fast-track model development and supports future creation of robust pan-cancer segmentation models. Leveraging these capabilities could accelerate clinical integration of pathology artificial intelligence tools and enable reproducible biomarker discovery.

Identifiers

PMID42086725
PMCPMC13195172

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.